Polar region water area temperature difference energy control method and power generation device

Abstract

The invention discloses a polar region water area temperature difference energy control method and a power generation device, and belongs to the technical field of temperature difference energy power generation. The control method comprises the steps that under the condition that the net output power of the power generation device is maximum, the optimal parameter sequence of different cold and heat source working conditions is determined; constructing an MPC prediction model by taking the rotation speed of a working medium pump and the rotation speed of an expansion machine as input parameters and the evaporation temperature and the condensation temperature as output results according to the incidence relation between the pressure of the cycle working medium and the evaporation temperature and the condensation temperature; constructing a target function according to the optimal parameter sequence and an output result of the MPC prediction model; and based on the MPC prediction model and the target function, the MPC prediction model is iteratively updated by adjusting the rotating speed of the working medium pump and the rotating speed of the expansion machine, the optimal rotating speed of the working medium pump and the optimal rotating speed of the expansion machine are determined, and the operation parameters of the power generation device are controlled. The operation parameters of the power generation device are adaptively adjusted through the working conditions of the cold and heat sources, so that the net output power of the power generation component is maximum, and the power generation efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of temperature difference energy power generation, and in particular to a polar water temperature difference energy control method and a power generation device. Background Art

[0002] Since the marine operation equipment deployed in polar waters is far away from the land, its power supply is an urgent problem to be solved. In order to solve the power supply problem of equipment in polar waters, it is proposed to use the temperature difference between the cold air on the ice surface of the Arctic Ocean and the sea water under the ice to convert it into electrical energy to power the relevant equipment.

[0003] In the prior art, there are two main ways to generate electricity by converting temperature difference: one is to use thermoelectric devices to directly convert temperature difference energy into electrical energy for use, and the other is to use steam power cycle to heat the circulating working fluid to make it undergo phase change and become steam with higher temperature and pressure. After the steam enters the steam turbine or expander, it drives its rotor to rotate and then drags the power generation components to generate electricity. Temperature difference energy thermoelectric conversion devices generally have problems such as low thermoelectric conversion efficiency and low overall efficiency. Their thermoelectric conversion efficiency is generally lower than 5%. In addition, due to the heat dissipation loss of the hot end exchanger, the actual efficiency of the entire device is very low. At the same time, environmental changes in polar waters will also cause the operation of the power generation device to deviate from the set operating condition point, reducing the operating efficiency of the device. For example, the temperature change of low-temperature air as a cold source will change the pressure drop on the working fluid side of the condenser, affecting the exhaust back pressure of the expander. The increase in pressure drop will deteriorate the net output power of the expander and the performance of the system.

[0004] Therefore, there is an urgent need for a polar water temperature difference energy control method and a power generation device that can adaptively adjust the operating parameters of the power generation device through the operating parameters of the cold and hot sources, so that the net output power of the power generation components is maximized and the power generation efficiency is improved. Summary of the invention

[0005] In view of this, it is necessary to provide a polar water temperature difference energy control method and a power generation device, which can adaptively adjust the operating parameters of the power generation device through the operating parameters of the cold and heat sources, so as to maximize the net output power of the power generation components and improve the power generation efficiency.

[0006] In order to solve the above technical problems, on the one hand, a polar water temperature difference energy control method is applied to a polar water temperature difference energy power generation device, and the control method includes: Under the condition that the net output power of the power generation device is maximum, determining the optimal parameter sequence of different cold and hot source working conditions, the optimal parameter sequence includes an evaporation temperature setting value and a condensation temperature setting value, the evaporation temperature setting value is related to the heat source working condition, and the condensation temperature setting value is related to the cold source working condition; Taking the working fluid pump speed and the expander speed as input parameters, and the evaporation temperature and the condensation temperature as output results, an MPC prediction model is constructed according to the correlation between the pressure of the circulating working fluid and the evaporation temperature and the condensation temperature, wherein the pressure of the circulating working fluid is controlled by the working fluid pump speed and the expander speed; Constructing an objective function based on the optimal parameter sequence and the output result of the MPC prediction model; Based on the MPC prediction model and objective function, the MPC prediction model is iteratively updated by adjusting the working fluid pump speed and the expander speed to determine the optimal working fluid pump speed and the optimal expander speed; The operating parameters of the power generation device are controlled according to the optimal working fluid pump speed and the optimal expander speed.

[0007] In one possible implementation, under the condition that the net output power of the power generation device is maximum, the optimal parameter sequence for different cold and heat source working conditions is determined, including: Based on the least squares support vector machine model, an initial parameter sequence prediction model is constructed; Determine sample data of evaporation temperature and condensation temperature when the net output power of the power generation device is maximum under different cold and heat source operating parameters, and generate training sample data; Performing iterative optimization training on the initial parameter sequence prediction model according to the training sample data to generate a target parameter sequence prediction model; Based on the target parameter sequence prediction model, the optimal parameter sequence is determined according to the real-time cold and hot source operating parameters.

[0008] In a possible implementation, sample data of evaporation temperature and condensation temperature when the net output power of the power generation device is maximum under different cold and hot source operating parameters are determined to generate training sample data, including: Based on the organic Rankine cycle simulation model, a temperature difference energy power generation simulation model is constructed; Based on the temperature difference energy power generation simulation model, determine the net output power of the simulation model under different cold and hot source operating parameters, and generate a trend diagram of the net output power changing with the evaporation temperature and the condensation temperature; According to the transformation trend diagram, determining the evaporation temperature constraint interval and the condensation temperature constraint interval when the net output power is maximum; Based on the optimization algorithm, under the condition of maximum net output power, the optimal evaporation temperature and condensation temperature are determined according to the evaporation temperature constraint interval and the condensation temperature constraint interval; Repeat the simulation and optimization process, calculate the sample data of the optimal evaporation temperature and the optimal condensation temperature corresponding to different cold and hot source operating parameters, and generate training sample data.

[0009] In one possible implementation, the working fluid pump speed and the expander speed are used as input parameters, and the evaporation temperature and the condensation temperature are used as output results. According to the correlation between the pressure of the circulating working fluid and the evaporation temperature and the condensation temperature, an MPC prediction model is constructed, including: The working fluid pump speed and the expander speed are set as the input variables of the MPC prediction model, and the evaporation temperature prediction parameters and the condensation temperature prediction parameters in the future time domain are set as the output variables of the MPC prediction model; A simulation dynamic model is constructed based on the correlation between the pressure of the circulating working fluid in the power generation device and the evaporation temperature and condensation temperature as well as the input and output variables; Converting the simulation dynamic model into a state space equation; An MPC prediction model is constructed based on the state space equations and optimization constraints.

[0010] In a possible implementation, an objective function is constructed according to the optimal parameter sequence and the output result of the MPC prediction model, and the calculation formula of the objective function is:

[0011] in, is the objective function value, is the prediction output result of the k-th step of the MPC prediction model, is the optimal parameter, For the general Transpose the matrix. is the control input of the kth step, and is the weight matrix, which is used to balance the output tracking error and control energy consumption. To predict the horizon, determine how long the control component should look ahead. To control the time domain, it determines the length of time that the control component actually applies the control action. For the general Perform matrix transpose.

[0012] In one possible implementation, based on the MPC prediction model and the objective function, the MPC prediction model is iteratively updated by adjusting the working fluid pump speed and the expander speed to determine the optimal working fluid pump speed and the optimal expander speed, including: Setting optimization constraints for input and output of the MPC control model according to the objective function and the state space equation of the MPC prediction model; Based on the MPC prediction model, a predicted temperature sequence is output according to the current working fluid pump speed and the expander speed, wherein the predicted temperature sequence includes an evaporation temperature prediction parameter and a condensation temperature prediction parameter; Based on the objective function, calculating the objective function value between the predicted temperature sequence and the optimal parameter sequence; Based on the objective function value and the optimization constraints, the input parameters of the MPC prediction model are adjusted, the MPC prediction model is iteratively updated repeatedly, and the optimal working fluid pump speed and the optimal expander speed are determined according to a preset threshold.

[0013] In one possible implementation, the optimization constraints of the MPC control model input and output are set according to the objective function and the state space equation of the MPC prediction model, including: Minimizing the objective function is set as an optimization problem; Determining constraint conditions according to the state vector, input vector, and output vector in the state space matrix of the MPC control model; determining constraint conditions; The optimization problem and the constraints constitute optimization constraints.

[0014] In one possible implementation, the MPC prediction model is iteratively updated repeatedly to determine the optimal working fluid pump speed and the optimal expander speed according to a preset threshold, including: Calculate the objective function value between the output result of each iteration of the MPC prediction model and the optimal parameter sequence; Determine the optimal output result of the MPC prediction model according to the comparison result of the objective function value and the preset threshold value and the maximum number of iterations; The input parameters corresponding to the optimal output result of the MPC prediction model are the optimal working fluid pump speed and the optimal expander speed.

[0015] In a second aspect, the present invention further provides a polar water temperature difference energy power generation device, the power generation device comprising a control component and a power generation component, the control component storing a computer program, the computer program being used to implement the polar water temperature difference energy control method as described above, the polar waters comprising a deep water layer, an ice layer and an air layer contacting each other from bottom to top, the power generation component comprising: a heat source device, a cold source device, a regenerator and a power generation component; The heat source device, the heat regenerator and the power generation component are arranged in the deep water layer, the cold source device is arranged in the ice layer and the air layer, and the control component is arranged in the air layer; One end of the heat source device is connected to one end of the regenerator through a pipeline, and the other end is in contact with the deep water layer through a pipeline; the other end of the regenerator is connected to one end of the cold source device through a pipeline, and the other end of the cold source device is in contact with the air layer through a pipeline; the power generation component is mechanically connected to the heat source device; and the control component is electrically connected to the heat source device and the cold source device, respectively.

[0016] In a possible implementation, the heat source device includes a hot water pump, an evaporator, and an expander; the cold source device includes an air cooler, a liquid storage tank, and a working fluid pump; The evaporator, expander and regenerator are connected in sequence by pipelines; The regenerator, air cooler, liquid storage tank and working fluid pump are connected in sequence through pipelines; The air cooler is arranged in the air layer, and the liquid storage tank and the working fluid pump are arranged in the ice layer.

[0017] The beneficial effects of the present invention are as follows: first, under the condition that the net output power of the power generation device is maximum, an optimal parameter sequence is determined, the optimal parameter sequence includes an evaporation temperature setting value and a condensation temperature setting value, the evaporation temperature setting value is determined according to the heat source parameter, and the condensation temperature setting value is determined according to the cold source parameter; then, taking the working fluid pump speed and the expander speed as input parameters, taking the evaporation temperature and the condensation temperature as output results, and constructing an MPC prediction model according to the correlation between the pressure of the circulating working fluid and the evaporation temperature and the condensation temperature, wherein the pressure of the circulating working fluid is controlled by the working fluid pump speed and the expander speed; then, constructing an objective function according to the optimal parameter sequence and the output results of the MPC prediction model; then, based on the MPC prediction model and the objective function, by adjusting the working fluid pump speed and the expander speed, the MPC prediction model is iteratively updated to determine the optimal working fluid pump speed and the optimal expander speed; finally, controlling the operating parameters of the power generation device according to the optimal working fluid pump speed and the optimal expander speed. The present invention determines the set values ​​of the evaporation temperature and the condensation temperature when the net output power of the power generation device is maximum, calculates the output result of the MPC prediction model and the target function value of the set values ​​of the evaporation temperature and the condensation temperature through the objective function, and performs iterative training, and finally determines the optimal working fluid pump speed and the optimal expander speed, so that the net output power of the power generation device is maximized and at the optimal working point. The power generation device of the present invention can adapt to the changes of the cold and heat sources. Saving the operating parameters of the power generation device not only enhances the adaptability of the temperature difference energy power generation device to complex and changeable environments, but also improves the net output power of the power generation device, realizes the effective utilization of temperature difference energy resources and maximizes output. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 A schematic flow chart of an embodiment of the polar water temperature difference energy control method provided by the present invention; Figure 2 For the present invention Figure 1 A schematic flow chart of an embodiment of step S101; Figure 3 For the present invention Figure 2 A schematic flow chart of an embodiment of step S202; Figure 4 For the present invention Figure 1 A schematic flow chart of an embodiment of step S102; Figure 5 For the present invention Figure 1 A schematic flow chart of an embodiment of step S104; Figure 6 For the present invention Figure 5 A flowchart of an embodiment of step S504; Figure 7 A schematic diagram of a polar water temperature difference energy power generation device provided by the present invention. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0021] In the description of the embodiments of the present invention, unless otherwise specified, "multiple" means two or more than two. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" may mean: A exists alone, A and B exist at the same time, and B exists alone.

[0022] The descriptions of "first", "second", etc. involved in the embodiments of the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the technical features defined as "first" or "second" may explicitly or implicitly include at least one of the features.

[0023] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0024] The present invention provides a polar water temperature difference energy control method and a power generation device, which are described below respectively.

[0025] In some embodiments of the present invention, Figure 1 As shown, Figure 1 A schematic flow chart of an embodiment of a method for controlling temperature difference in polar waters provided by the present invention includes: S101, under the condition that the net output power of the power generation device is maximum, determining an optimal parameter sequence for different cold and hot source working conditions, the optimal parameter sequence comprising an evaporation temperature setting value and a condensation temperature setting value, the evaporation temperature setting value is related to the heat source working condition, and the condensation temperature setting value is related to the cold source working condition; S102, using the working fluid pump speed and the expander speed as input parameters, and the evaporation temperature and the condensation temperature as output results, and constructing an MPC prediction model according to the correlation between the pressure of the circulating working fluid and the evaporation temperature and the condensation temperature, wherein the pressure of the circulating working fluid is controlled by the working fluid pump speed and the expander speed; S103, constructing an objective function according to the optimal parameter sequence and the output result of the MPC prediction model; S104, based on the MPC prediction model and the objective function, by adjusting the working fluid pump speed and the expander speed, the MPC prediction model is iteratively updated to determine the optimal working fluid pump speed and the optimal expander speed; S105. Controlling the operating parameters of the power generation device according to the optimal working fluid pump speed and the optimal expander speed.

[0026] It should be noted that the polar water temperature difference energy power generation device includes a working fluid pump, an expander, a circulating working fluid, an evaporator and an air cooler, etc. The air cooler is a condenser equipped with an electric-driven forced convection method, the evaporation temperature is the working temperature of the evaporator, the condensation temperature is the saturation temperature at which the circulating working fluid in the condenser is converted from gas to liquid, the working fluid pump speed is the working parameter of the working fluid pump, the expander speed is the working parameter of the expander, the cold source parameters of the polar water temperature difference energy power generation device include the cold source temperature and the cold source flow rate, the heat source parameters include the heat source temperature and the heat source flow rate; the cold source temperature The temperature and flow rate of the cold source affect the condensing temperature of the air cooler of the temperature difference energy power generation device, and the temperature and flow rate of the heat source affect the evaporation temperature of the evaporator of the temperature difference energy power generation device; the net output power of the temperature difference energy power generation device is equal to the output power of the expander minus the power consumption of the working fluid pump; the speed of the working fluid pump will affect the flow rate of the circulating working fluid, and then affect the evaporation pressure. The increase in the speed of the expander will improve the power generation efficiency and increase the amount of gaseous circulating working fluid absorbed. More gaseous circulating working fluid is required to enter the expander. Therefore, the pressure of the circulating working fluid is affected by the speed of the working fluid pump and the speed of the expander.

[0027] This embodiment determines the set values ​​of the evaporation temperature and the condensation temperature of the temperature difference energy power generation device when the net output power is maximum, and through the objective function, calculates the output result of the MPC prediction model and the objective function value of the set values ​​of the evaporation temperature and the condensation temperature for iterative training, and finally determines the optimal working fluid pump speed and the optimal expander speed to maximize the net output power of the power generation device and to be at the optimal working point. The power generation device of the present invention can adaptively adjust the operating parameters of the power generation device according to the changes in the cold and heat sources, which not only enhances the adaptability of the temperature difference energy power generation device to complex and changeable environments, but also improves the net output power of the power generation device, thereby realizing the effective utilization of temperature difference energy resources and maximizing output.

[0028] In some embodiments of the present invention, Figure 2 As shown, Figure 2 The present invention provides Figure 1 The flowchart of an embodiment of step S101 in FIG. 1 includes: S201, constructing an initial parameter sequence prediction model based on a least squares support vector machine model; S202, determining sample data of evaporation temperature and condensation temperature when the net output power of the power generation device is maximum under different cold and heat source operating parameters, and generating training sample data; S203, iteratively training the initial parameter sequence prediction model according to the training sample data to generate a target parameter sequence prediction model; S204: Based on the target parameter sequence prediction model, the optimal parameter sequence is determined according to the real-time cold and hot source operating parameters.

[0029] This embodiment obtains sample data of the evaporation temperature and condensation temperature of the power generation device when the net output power is maximum, iteratively trains the least squares support vector machine model, and obtains a parameter sequence prediction model. The parameter sequence prediction model can predict the evaporation temperature and condensation temperature when the power is maximum based on the real-time operating parameters of the cold and hot sources.

[0030] In some embodiments of the present invention, Figure 3 As shown, Figure 3 The present invention provides Figure 2 The flowchart of an embodiment of step S202 in FIG. 1 includes: S301. Constructing a temperature difference energy power generation simulation model based on an organic Rankine cycle simulation model; S302, based on the temperature difference energy power generation simulation model, calculating the net output power of the simulation model with different cold and heat source operating parameters, and generating a trend diagram of the net output power changing with the evaporation temperature and the condensation temperature; S303, determining an evaporation temperature constraint interval and a condensation temperature constraint interval when the net output power is maximum according to the transformation trend graph; S304, based on the optimization algorithm, under the condition of maximum net output power, determining the optimal evaporation temperature and condensation temperature according to the evaporation temperature constraint interval and the condensation temperature constraint interval; S305, repeat the simulation and optimization process to determine sample data of evaporation temperature and condensation temperature under different cold and hot source operating parameters, and generate training sample data.

[0031] It should be noted that the organic Rankine cycle is a thermodynamic cycle that uses organic working fluids (such as cyclopentane, R245fa, etc.) instead of water as the circulating fluid. It is suitable for low-temperature and medium-temperature heat sources and completes the conversion of thermal energy into mechanical energy through four main processes: evaporation, expansion, condensation and compression.

[0032] Specifically, in the temperature difference energy power generation simulation model, different cold and heat source operating parameters are set while ensuring a constant circulating working fluid flow rate. Each simulation only changes one condition in the cold and heat source operating parameters, and the net output power of the corresponding temperature difference energy power generation simulation model is obtained through simulation calculation. After multiple simulation experiments, the net output power of the temperature difference energy power generation simulation model is analyzed as a function of the evaporation temperature and the condensation temperature. According to the change trend diagram, the constraint range of the evaporation temperature and the constraint range of the condensation temperature when the net output power is maximum are obtained, and then combined with the operating parameter constraints of the temperature difference energy power generation simulation model, a complete constraint condition is obtained. With the maximization of the net output power of the power generation device as the optimization goal, the corresponding evaporation temperature and condensation temperature under different cold and heat source operating parameters are calculated through the optimization algorithm, and then multiple groups of sample data of evaporation temperature and condensation temperature under different cold and heat source operating parameters are obtained.

[0033] Furthermore, the operating parameter constraints of the temperature difference energy power generation simulation model are as follows: the evaporation temperature should be lower than the critical temperature of the circulating working fluid and lower than the heat source inlet temperature, and the temperature difference between the evaporation temperature and the heat source inlet temperature should be greater than the sum of the evaporator pinch point temperature difference and the working fluid superheat; the condensation temperature should be higher than the cold source inlet temperature, i.e., the ambient temperature, and the temperature difference between the condensation temperature and the cold source inlet temperature should be greater than the sum of the condenser pinch point temperature difference and the working fluid supercooling; in this embodiment, in order to ensure the normal operation of the system, the temperature difference between the evaporation temperature and the condensation temperature is set to at least 5K.

[0034] This embodiment simulates different cold and heat source operating parameters through an organic Rankine cycle simulation model, and obtains multiple sets of sample data of evaporation temperature and condensation temperature under different cold and heat source operating parameters under the condition of maximum net output power of the model, which provides a training data set for the subsequent training of the parameter sequence prediction model, improves the set values ​​of the evaporation temperature sequence and condensation temperature output by the parameter sequence prediction model, and maximizes the output efficiency of the power generation device.

[0035] In some embodiments of the present invention, Figure 4 As shown, Figure 4 The present invention provides Figure 1 The flowchart of an embodiment of step S103 in the embodiment includes: S401, setting the working fluid pump speed and the expander speed as input variables of the MPC prediction model, and setting the evaporation temperature prediction parameter and the condensation temperature prediction parameter in the future time domain as output variables of the MPC prediction model; S402, constructing a simulation dynamic model according to the correlation between the pressure of the circulating working fluid in the power generation device and the evaporation temperature and the condensation temperature and the input and output variables; S403, converting the simulation dynamic model into a state space equation; S404: construct an MPC prediction model according to the state space equation and the optimization constraints.

[0036] It should be noted that the core idea of ​​the MPC predictive control model is to establish a dynamic model of the system, based on current and past input and output data, as well as system constraints, to predict the system state in the future, and optimize the control strategy at the current moment. Specifically, when it comes to the prediction of the evaporation temperature and condensation temperature of the power generation device, the MPC control component will treat these parameters as part of the system state or input, and perform rolling optimization and prediction.

[0037] Specifically, the state space method or other system modeling methods are used to describe the power generation device as a multivariable dynamic system. This model will include key parameters such as evaporation temperature, condensation temperature, working fluid pump speed and expander speed, and take into account the physical characteristics and thermodynamic principles of the power generation device to ensure that the model can accurately reflect the dynamic behavior of the system. The main components include the following parts: the evaporator describes the phase change process of the working fluid from liquid to gas; the turbine describes the process of the working fluid expanding and doing work; the air cooler describes the phase change process of the working fluid from gas to liquid; the regenerator describes the heat exchange process between the working fluids; the working fluid pump describes the process of pressurizing the liquid working fluid into the evaporator. These components are represented by a set of differential equations or state space models, and then these equations are combined to finally obtain the following MPC prediction model equations: , , in, is the state vector The first-order derivative of represents the rate of change of the MPC prediction model state over time, is the state vector of the MPC prediction model; is the control input vector; is the output vector of the MPC prediction model; , is the state space matrix of the MPC prediction model, is the input of the MPC prediction model, where is the speed of the working fluid pump, is the speed of the expander; ,in is the evaporation temperature, is the condensation temperature.

[0038] This embodiment constructs an MPC prediction model and comprehensively considers multiple input parameters including evaporation temperature prediction parameters, condensation temperature prediction parameters, working fluid pump speed, and expander speed to predict the evaporation temperature and condensation temperature in the future time domain, thereby improving the accuracy of the prediction results.

[0039] In some embodiments of the present invention, the calculation formula of the objective function is: , in, is the objective function value, is the prediction output result of the k-th step of the MPC prediction model, is the optimal parameter, For the general Transpose the matrix. is the control input of the kth step, and is the weight matrix, which is used to balance the output tracking error and control energy consumption. To predict the horizon, determine how long the control component should look ahead. To control the time domain, it determines the length of time that the control component actually applies the control action. For the general Perform matrix transpose.

[0040] In some embodiments of the present invention, Figure 5 As shown, Figure 5 The present invention provides Figure 1 The flowchart of an embodiment of step S104 in the embodiment includes: S501, setting optimization constraints of the input and output of the MPC control model according to the objective function and the state space equation of the MPC prediction model; S502, based on the MPC prediction model, output a predicted temperature sequence according to the current working fluid pump speed and the expander speed, the predicted temperature sequence including an evaporation temperature prediction parameter and a condensation temperature prediction parameter; S503, calculating the objective function value between the predicted temperature sequence and the optimal parameter sequence based on the objective function; S504: Based on the objective function value and the optimization constraints, the input parameters of the MPC prediction model are adjusted, the MPC prediction model is iteratively updated repeatedly, and the optimal working fluid pump speed and the optimal expander speed are determined according to a preset threshold.

[0041] Specifically, by changing the input parameters of the MPC prediction model, namely the working fluid pump speed and the expander speed, a control parameter sequence of the evaporation temperature parameters and the condensation temperature parameters in the future time domain interval is obtained. The first element in the control parameter sequence of the evaporation temperature parameters and the condensation temperature parameters is the current evaporation temperature parameters and the condensation temperature parameters. The objective function value between the current evaporation temperature parameters and the condensation temperature parameters and the optimal parameters is calculated. The closer the objective function value is to 0, the closer the current working fluid pump speed and the expander speed are to the optimal state. A preset threshold is set, and combined with the limit on the number of iterations, it is determined that the evaporation temperature parameters and the condensation temperature parameters output by the MPC prediction model are closest to the optimal parameters. At this time, the input parameters of the MPC prediction model, namely the working fluid pump speed and the expander speed, are the optimal operating parameters of the power generation components.

[0042] This embodiment iteratively trains the model by calculating the objective function value between the output result of the MPC prediction model and the set values ​​of the optimal parameters of the evaporation temperature and the condensation temperature, and takes minimizing the objective function as the optimization problem to determine the optimal working fluid pump speed and the optimal expander speed, so as to maximize the net output power of the power generation device, improve the net output power of the power generation device, and realize the effective utilization of temperature difference energy resources and maximize output.

[0043] In some embodiments of the present invention, the optimization constraints of the MPC control model input and output are set according to the objective function and the state space equation of the MPC prediction model, including: The minimization of the objective function is set as an optimization problem; Determine the constraint conditions according to the state vector, input vector and output vector in the state space matrix of the MPC control model; The optimization problem and constraints constitute the optimization constraints.

[0044] Specifically, the optimization problem is to minimize the objective function. By controlling the input parameters of the model, the output result of the MPC prediction model is made as close as possible to the parameter sequence prediction model output corresponding to the parameter MPC prediction model. In this way, the model is continuously updated and iterated to gradually reduce the prediction error and realize closed-loop optimization control.

[0045] Furthermore, the specific optimization constraints are as follows: , in, , is the state space matrix of the MPC prediction model, is the state vector of the MPC prediction model, is the state vector of the MPC prediction model at the next moment, is the control input vector, is the output vector of the MPC prediction model, and To control the minimum and maximum values ​​of the input vector, and are the constrained minimum and maximum values ​​of the state vector, and are the constrained minimum and maximum values ​​of the output vector.

[0046] This embodiment sets the constraint conditions by minimizing the objective function as the optimization problem and combining the restrictions on the critical values ​​of the model operation parameters to ensure that the MPC prediction model performs predictions within the constraints and that the parameters of the predicted output put the power generation device in the best working state.

[0047] In some embodiments of the present invention, Figure 6 As shown, Figure 6 The present invention provides Figure 5 The flowchart of an embodiment of step S504 in the embodiment includes: S601, calculating the objective function value between the output result of each iteration of the MPC prediction model and the optimal parameter sequence; S602, determining the optimal output result of the MPC prediction model according to the comparison result between the objective function value and the preset threshold value and the maximum number of iterations; S603. The input parameters corresponding to the optimal output result of the MPC prediction model are the optimal working fluid pump speed and the optimal expander speed.

[0048] This embodiment calculates the objective function value between the output result of each iteration of the MPC prediction model and the set values ​​of the optimal parameters of the evaporation temperature and the condensation temperature, and takes minimizing the objective function as the optimization problem to determine the optimal working fluid pump speed and the optimal expander speed, so as to maximize the net output power of the power generation device, improve the net output power of the power generation device, and realize the effective utilization of temperature difference energy resources and maximize the output.

[0049] The present invention also provides a polar water temperature difference energy power generation device, such as Figure 7 As shown, Figure 7 A schematic diagram of a polar water temperature difference energy power generation device provided by the present invention, the power generation device includes a control component and a power generation component, the control component stores a computer program, the computer program is used to implement a polar water temperature difference energy control method, the polar water area includes a deep water layer, an ice layer and an air layer that are in contact with each other from bottom to top, and the power generation component includes: a heat source device 11, a cold source device 12, a regenerator 5 and a power generation component 7; The heat source device 11, the regenerator 5 and the power generation component 7 are arranged in the deep water layer, the cold source device 12 is arranged in the ice layer and the air layer, and the control component is arranged in the air layer; One end of the heat source device 11 is connected to one end of the regenerator 5 through a pipeline, and the other end is in contact with the deep water layer through a pipeline; the other end of the regenerator 5 is connected to one end of the cold source device 12 through a pipeline, and the other end of the cold source device 12 is in contact with the air layer through a pipeline; the power generation component 7 is connected to the heat source device 11 through a pipeline; and the control component is electrically connected to the heat source device 11 and the cold source device 12, respectively.

[0050] Specifically, this embodiment utilizes the temperature difference between the low-temperature air in the polar waters and the waters under the ice layer to expand and contract the circulating working fluid, and converts the temperature difference heat energy into electrical energy. The heat source device 11 absorbs the heat of the deep water layer in the water area, heats the circulating working fluid to evaporate and gasify it, and the heating energy is converted into the pressure of the circulating working fluid. The pressure of the circulating working fluid drives the power generation component 7 to generate electricity and convert it into electrical energy. The regenerator 5 recovers the residual heat of the vaporized circulating working fluid, and sends the vaporized circulating working fluid into the cold source device 12. The low-temperature air in the cold source device 12 cools the circulating working fluid into a liquid state. The liquid circulating working fluid is then sent to the regenerator 5, and the liquid circulating working fluid is preheated by using the residual heat absorbed by the regenerator 5, and then sent to the heat source device 11 for heating and vaporization, thereby converting the temperature difference energy into electrical energy.

[0051] This embodiment recovers the residual heat in the heat source device through the regenerator, and uses the residual heat to preheat the circulating working fluid, thereby improving the conversion efficiency of temperature difference energy.

[0052] In some embodiments of the present invention, the heat source device includes a hot water pump 9, an evaporator 8, and an expander 6; the cold source device 12 includes an air cooler 1, a liquid storage tank 3, and a working fluid pump 4; The evaporator 8, the expander 6 and the regenerator 5 are connected in sequence by pipelines; The regenerator 5, the air cooler 1, the liquid storage tank 3 and the working fluid pump 4 are connected in sequence by pipelines; The air cooler 1 is arranged in the air layer, and the liquid storage tank 3 and the working fluid pump 4 are arranged in the ice layer.

[0053] It should be noted that the polar waters temperature difference energy power generation device also includes a temperature difference energy power generation device support structure 2, which is used to support the entire temperature difference energy power generation device.

[0054] It should be noted that the air cooler is a condenser equipped with an electrically driven forced convection component. The expander selects a screw expander, which has the advantages of small size, light weight, small exhaust pulsation, simple structure, few parts, few wearing parts, long life, and reliable operation. The screw expander can operate without a foundation, runs smoothly, has low vibration, low noise, long unit life, low maintenance cost, and high safety and reliability. The evaporator selects a shell and tube falling film evaporator with good heat transfer performance, which has the advantages of low price, mature technology, small heat transfer temperature difference, good heat transfer performance, small working fluid filling amount and compact structure. The working fluid pump adopts a diaphragm metering pump, which can meet the needs of various process flows and provide accurate output flow. The flow rate can be adjusted steplessly within the range of 0% to 100%. The diaphragm metering pump can achieve no leakage when conveying the medium, and has the characteristics of corrosion resistance, wear resistance and pressure resistance.

[0055] Specifically, the hot water pump 9 of the polar water temperature difference energy power generation device absorbs water from the deep water layer, outputs heat to the absorbed water in the evaporator 8, and is then discharged into the sea by the hot water pump 9. The circulating working fluid is heated by the heat of the water in the deep water layer in the evaporator 8 and evaporates into a gas. The gaseous circulating working fluid produces a circulating working fluid pressure change, which drives the expander 6 to operate. The expander 6 converts the internal energy of the gaseous circulating working fluid into mechanical energy, which drives the power generation component 7 to generate electricity. The regenerator 5 recovers the remaining gaseous circulating working fluid in the expander 6, and after recovering the remaining heat, it enters the air cooler 1 for air cooling. Device 1 adopts an electrically driven forced convection method, using the cold in the air layer to cool the gaseous circulating working fluid. The cooled liquid circulating working fluid is sent to the liquid storage tank 3 for storage. The working fluid pump 4 compresses the liquid circulating working fluid in the liquid storage tank 3 and enters the regenerator 5. The residual heat recovered in the regenerator 5 is preheated, and then enters the evaporator 8 to be evaporated by the heat in the deep water layer, and circulates in sequence. The control component controls the working fluid pump speed and the expander speed of the temperature difference energy power generation device through the real-time cold and heat source operating parameters. The control component is not shown in the figure.

[0056] Furthermore, the control component controls the working fluid pump speed and the expander speed of the temperature difference energy power generation device by constructing a chip of a hierarchical supervisory control model, so that the temperature difference energy power generation device is in an optimal working state and outputs maximum power; the hierarchical supervisory control model is divided into two parts. The first part is to determine the optimal evaporation temperature and the optimal condensation temperature setting values ​​according to the real-time cold and heat source working conditions parameters through a trained least squares support vector machine; the second part is an MPC prediction model, and an objective function is constructed according to the output result of the MPC prediction model and the optimal evaporation temperature and the optimal condensation temperature setting values. By adjusting the input parameters of the MPC prediction model, namely the working fluid pump speed and the expander speed, an optimization solution is performed with the objective function minimized, and the output result of the MPC prediction model is obtained, which is closest to the optimal evaporation temperature and the optimal condensation temperature setting values. At this time, the working fluid pump speed and the expander speed are the optimal operating parameters of the temperature difference energy power generation device.

[0057] This embodiment adopts an electric-driven forced convection air cooler to enhance the heat exchange efficiency between low-temperature air and the circulating working fluid in the air cooler; the regenerator recovers the residual heat after the expander does work, and transfers this heat to the liquid circulating working fluid from the working fluid pump, thereby improving the conversion efficiency of temperature difference energy; the hot water pump can continuously input seawater into the evaporator as a heat source for the temperature difference energy power generation system, preventing the evaporator from freezing on its surface after heat release, thereby ensuring the conversion efficiency of temperature difference energy; the controller adjusts the working fluid pump speed and the expander speed according to the real-time cold and heat source operating parameters, so that the temperature difference energy power generation device is in the optimal working state and the output power is maximized.

[0058] The polar water temperature difference energy control method and power generation device provided by the present invention are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A polar water temperature difference energy control method, applied to a polar water temperature difference energy power generation device, characterized in that: The control method comprises: Under the condition that the net output power of the power generation device is maximum, determining the optimal parameter sequence of different cold and hot source working conditions, the optimal parameter sequence includes an evaporation temperature setting value and a condensation temperature setting value, the evaporation temperature setting value is related to the heat source working condition, and the condensation temperature setting value is related to the cold source working condition; Taking the working fluid pump speed and the expander speed as input parameters, and the evaporation temperature and the condensation temperature as output results, an MPC prediction model is constructed according to the correlation between the pressure of the circulating working fluid and the evaporation temperature and the condensation temperature, wherein the pressure of the circulating working fluid is controlled by the working fluid pump speed and the expander speed; Constructing an objective function based on the optimal parameter sequence and the output result of the MPC prediction model; Based on the MPC prediction model and objective function, the MPC prediction model is iteratively updated by adjusting the working fluid pump speed and the expander speed to determine the optimal working fluid pump speed and the optimal expander speed; The operating parameters of the power generation device are controlled according to the optimal working fluid pump speed and the optimal expander speed.

2. The polar water temperature difference energy control method according to claim 1, characterized in that: Under the condition of maximum net output power of the power generation device, determine the optimal parameter sequence for different cold and hot source conditions, including: Based on the least squares support vector machine model, an initial parameter sequence prediction model is constructed; Determine sample data of evaporation temperature and condensation temperature when the net output power of the power generation device is maximum under different cold and heat source operating parameters, and generate training sample data; Performing iterative optimization training on the initial parameter sequence prediction model according to the training sample data to generate a target parameter sequence prediction model; Based on the target parameter sequence prediction model, the optimal parameter sequence is determined according to the real-time cold and hot source operating parameters.

3. The polar water temperature difference energy control method according to claim 2, characterized in that: Determine the sample data of evaporation temperature and condensation temperature when the net output power of the power generation device is maximum under different cold and heat source operating parameters, and generate training sample data, including: Based on the organic Rankine cycle simulation model, a temperature difference energy power generation simulation model is constructed; Based on the temperature difference energy power generation simulation model, determine the net output power of the simulation model under different cold and hot source operating parameters, and generate a trend diagram of the net output power changing with the evaporation temperature and the condensation temperature; According to the transformation trend diagram, determining the evaporation temperature constraint interval and the condensation temperature constraint interval when the net output power is maximum; Based on the optimization algorithm, under the condition of maximum net output power, the optimal evaporation temperature and condensation temperature are determined according to the evaporation temperature constraint interval and the condensation temperature constraint interval; Repeat the simulation and optimization process, calculate the sample data of the optimal evaporation temperature and the optimal condensation temperature corresponding to different cold and hot source operating parameters, and generate training sample data.

4. The polar water temperature difference energy control method according to claim 1, characterized in that: With the working fluid pump speed and expander speed as input parameters, and the evaporation temperature and condensation temperature as output results, an MPC prediction model is constructed according to the correlation between the pressure of the circulating working fluid and the evaporation temperature and condensation temperature, including: The working fluid pump speed and the expander speed are set as the input variables of the MPC prediction model, and the evaporation temperature prediction parameters and the condensation temperature prediction parameters in the future time domain are set as the output variables of the MPC prediction model; A simulation dynamic model is constructed based on the correlation between the pressure of the circulating working fluid in the power generation device and the evaporation temperature and condensation temperature as well as the input and output variables; Converting the simulation dynamic model into a state space equation; An MPC prediction model is constructed based on the state space equations and optimization constraints.

5. The polar water temperature difference energy control method according to claim 4, characterized in that: The objective function is constructed according to the output results of the optimal parameter sequence and the MPC prediction model. The calculation formula of the objective function is: in, is the objective function value, is the prediction output result of the k-th step of the MPC prediction model, is the optimal parameter, For the general Transpose the matrix, is the control input of the kth step, and is the weight matrix, which is used to balance the output tracking error and control energy consumption. To predict the horizon, determine how long the control component should look ahead. To control the time domain, it determines the length of time that the control component actually applies the control action. For the general Perform matrix transpose.

6. The polar water temperature difference energy control method according to claim 5, characterized in that: Based on the MPC prediction model and objective function, the MPC prediction model is iteratively updated by adjusting the working fluid pump speed and the expander speed to determine the optimal working fluid pump speed and the optimal expander speed, including: Setting optimization constraints for input and output of the MPC control model according to the objective function and the state space equation of the MPC prediction model; Based on the MPC prediction model, a predicted temperature sequence is output according to the current working fluid pump speed and the expander speed, wherein the predicted temperature sequence includes an evaporation temperature prediction parameter and a condensation temperature prediction parameter; Based on the objective function, calculating the objective function value between the predicted temperature sequence and the optimal parameter sequence; Based on the objective function value and the optimization constraints, the input parameters of the MPC prediction model are adjusted, the MPC prediction model is iteratively updated repeatedly, and the optimal working fluid pump speed and the optimal expander speed are determined according to a preset threshold.

7. The polar water temperature difference energy control method according to claim 6, characterized in that: The optimization constraints of the MPC control model input and output are set according to the objective function and the state space equation of the MPC prediction model, including: Minimizing the objective function is set as an optimization problem; Determining constraint conditions according to a state vector, an input vector, and an output vector in a state space matrix of the MPC control model; The optimization problem and the constraints constitute optimization constraints.

8. The polar water temperature difference energy control method according to claim 6, characterized in that: Repeatedly iterate and update the MPC prediction model, and determine the optimal working fluid pump speed and the optimal expander speed according to the preset threshold, including: Calculate the objective function value between the output result of each iteration of the MPC prediction model and the optimal parameter sequence; Determine the optimal output result of the MPC prediction model according to the comparison result of the objective function value and the preset threshold value and the maximum number of iterations; The input parameters corresponding to the optimal output result of the MPC prediction model are the optimal working fluid pump speed and the optimal expander speed.

9. A polar water temperature difference energy power generation device, the power generation device comprising a control component and a power generation assembly, the control component storing a computer program, the computer program being used to implement the steps of the polar water temperature difference energy control method as claimed in any one of claims 1 to 8, the polar waters comprising a deep water layer, an ice layer and an air layer contacting each other from bottom to top, characterized in that: The power generation assembly includes: a heat source device, a cold source device, a heat regenerator and a power generation component; The heat source device, the heat regenerator and the power generation component are arranged in the deep water layer, the cold source device is arranged in the ice layer and the air layer, and the control component is arranged in the air layer; One end of the heat source device is connected to one end of the regenerator through a pipeline, and the other end is in contact with the deep water layer through a pipeline; the other end of the regenerator is connected to one end of the cold source device through a pipeline, and the other end of the cold source device is in contact with the air layer through a pipeline; the power generation component is mechanically connected to the heat source device; and the control component is electrically connected to the heat source device and the cold source device, respectively.

10. The polar waters temperature difference energy power generation device according to claim 1, characterized in that: The heat source device includes a hot water pump, an evaporator, and an expander; the cold source device includes an air cooler, a liquid storage tank, and a working fluid pump; The evaporator, expander and regenerator are connected in sequence by pipelines; The regenerator, air cooler, liquid storage tank and working fluid pump are connected in sequence through pipelines; The air cooler is arranged in the air layer, and the liquid storage tank and the working fluid pump are arranged in the ice layer.

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